code: def test_support(distname, args, expected):
      # test that the support is updated if truncation and loc/scale are applied
      # use beta distribution since it is a transformed betaprime distribution,
      # so it is important that the correct support is considered
      # (i.e., the support of beta is (0,1), while betaprime is (0, inf))
      dist = getattr(stats, distname)(*args)
      rng = FastGeneratorInversion(dist)
      assert_array_equal(rng.support(), expected)
      rng.loc = 1
      rng.scale = 2
      assert_array_equal(rng.support(), 1 + 2 * np.array(expected))
  
imports: import pytest
  import warnings
  import numpy
  from numpy.testing import assert_array_equal, assert_allclose, suppress_warnings
  from copy import deepcopy
  from scipy.stats.sampling import FastGeneratorInversion
  from scipy import stats
label_desc: Use image node_id 10 for calling a global function with example usage: test_support(distname, args, expected) without return types